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This paper explores how longevity shocks transmit to corporate debt markets. We show that changes in life expectancy propagate to corporate debt via life insurers through their adjustment of the duration of their corporate bond holdings to match the duration of their liabilities. Life insurers demand more long-term bonds when longevity increases unexpectedly. Their demand of bonds of specific maturities affects corporate term spreads. Corporations exploit the predictable variation in term spreads by adjusting new debt maturities in response to longevity shocks. The debt response is concentrated among insurer-dependent firms and those with investment-grade ratings, which life insurers prefer.
Changes in bond duration of life insurers and longevity risk. The blue solid line shows changes in average duration of life insurers’ bond holdings, while the red dashed line indicates longevity risk.
The El Niño–Southern Oscillation (ENSO) is a major driver of global climate variability, yet its long-term effect on life expectancy remains unclear. Here, we quantify how ENSO persistently impedes mortality improvement, leading to considerable life expectancy and economic losses across high-income Pacific Rim countries. We estimate life expectancy losses of 0.5 years (monetary equivalent loss of $2.6 trillion) for the 1982–83 El Niño and 0.4 years ($4.7 trillion) for the 1997–98 event. Climate projections under moderate emission pathways suggest a cumulative decline of 2.8 years in life expectancy by 2100, amounting to $35 trillion losses, with most of the monetary burden falling on the middle-aged population. These findings reveal that intensifying ENSO variability poses an underrecognized and enduring threat to human health and socioeconomic stability, underscoring the urgent need for targeted adaptation strategies to safeguard population well-being.
Lag effect of El Niño events on mortality improvement and life expectancy.
Xu, Y.*, Tan, K. S., Pan, G, Porth, L., and Zhu, W. (2021). Sustainable Area-Yield Insurance Program with Optimal Risk Pooling: A Behavior-Based Machine Learning Approach. [SSRN], [PDF].
Area-yield insurance offers a sustainable alternative to traditional individual-loss programs by reducing moral hazard, administrative costs, and data requirements, but its efficiency is often limited by basis risk. This paper proposes a behavior- based machine learning approach to optimally form risk pools, improving contract performance. We first determine the optimal number of risk pools through farmers’ behaviour under a utility maximization framework, then apply unsupervised spectral clustering to group farmers with similar risk histories. This framework enhances the efficiency of the area-yield insurance contract and addresses challenges of high- dimensionality and computational complexity. Using corn and soybean production at both county- and farm-level in the U.S. Heartland, we show that our method significantly reduces basis risk and farmers’ tail risk, with potential profit gains of up to $2.8 billion for corn production.
Keywords: Sustainability, Area yield, Basis risk, Crop insurance, Machine learning.
Xu, Y.*, Tan, K. S., and Zhu, W. (2022). A Geo-Hierarchical Deep Learning Approach for Flood Insurance Modeling and Pricing. [SSRN], [PDF] (Previously titled "Borrowing Information Across Space and Time: Pricing Flood Risk with Physics-based Hierarchical Machine Learning Models")
Flood risk is an increasingly urgent concern as climate change intensifies the frequency and severity of extreme events. Flood insurance is a key tool to protect against such risk, but its modeling and pricing remain challenging. This paper proposes a Geo-Hierarchical Deep Learning (GHDL) framework, a scalable, interpretable, and cost-efficient approach, to generate high-resolution flood hazard factors and inform insurance pricing. The GHDL framework can effectively capture spatial dependencies that are essential for an accurate flood risk assessment by incorporating geographical connectivity directly into deep learning models. Using data from the Mississippi River Basin, we show that GHDL achieves 86%–91% accuracy in extreme scenarios, outperforming climate-uninformed and benchmark deep learning models. Applied to the National Flood Insurance Program (NFIP), GHDL-derived hazard factors reduce net premiums by 33.5% and solvency capital requirements by 32.3%. These improvements enhance pricing adequacy and contribute to the long-term sustainability of public flood insurance programs.
Keywords: Flood risk, Climate risk, Deep learning, Ratemaking, Colvency capital requirement.
Feng, Q., Jaidee, S.**, Pan, G., and Zhu, W. (2022). Robust Testing in High-Dimensional Linear Model, Working Paper.
We propose a joint significance of many coefficients in high-dimensional linear regression that is robust to unconditional heteroskedasticity and autocorrelation of unknown form within regression errors. Exploiting information on the limiting behaviors of an equally weighted empirical spectral distribution with its weighted counterpart, we derive the asymptotic property of proposed statistics without a priori knowledge of the sparsity level of tested parameters and the underlying structure of regression errors. Instead, we adopt the Random Matrix Theory and self-normalization technique to comprehend the curse of dimensionality, heteroskedasticity, and autocorrelation simultaneously. Thanks to the naturality of parametric framework, the limiting Gaussian distribution of our proposed statistics are free from the choice of kernel functions and bandwidth selections. Simulation studies confirm the accuracy of type-I error with satisfactory empirical testing power in a finite sample over many different types of dependent data such as time-series data and spatially dependent data. In practice, to avoid an upward size distortion from using conventional statistics, we recommend practitioners take the curse of dimensionality into account when the intensity ratio is greater than 0.05.
Chen, Z., Lou, P. and Zhu, W. (2021). Duration-Hedging Trades, Return Momentum and Reversal. Working paper. [SSRN], [PDF].
We study the duration-hedging trades of duration-sensitive strategic investors, i.e., pensions and life insurers. We use longevity shocks to identify their duration-hedging trades. Longevity shocks affect these investors’ liability duration and induce them to adjust their asset duration. When longevity shocks are low (high), they buy more short- (long-) duration stocks and sell more long- (short-) duration stocks. Because prior winners (losers) have shorter (longer) duration, they behave like momentum (con- trarian) traders when longevity shocks are low (high). We further verify this channel using capital flows and cross-state longevity variations.
Changes in stock duration of pensions and longevity risk. This plot shows the changes in duration of pensions' stock holdings (the blue dashed line), together with longevity risk (the red solid line).
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